Video content search using captioning data

ABSTRACT

A method includes identifying, at a computing device, multiple segments of video content based on a context sensitive term. Each segment of the multiple segments is associated with captioning data of the video content. The method also includes determining, at the computing device, first contextual information of a first segment of the multiple segments based on a set of factors. The method further includes comparing the first contextual information to particular contextual information that corresponds to content of interest. The method further includes in response to a determination that the first contextual information matches the particular contextual information, storing a first searchable tag associated with the first segment.

FIELD OF THE DISCLOSURE

The present disclosure is generally related to video content search.

BACKGROUND

A digital video recorder (DVR) is an electronic device used to recordvideos (e.g., television programming, movies, etc.) that a user device(e.g., a set-top box) receives from a content provider. When a DVRrecords a program, the DVR may also record captioning data associatedwith the program. The user may watch the DVR recordings at a later time.When the user wants to watch videos containing particular topics ofinterest from the DVR recordings, the user may manually search throughsome of the DVR recordings (e.g., the user may watch at least a portionof each of the DVR recordings) until one or more videos containing theparticular topics of interest are found.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating a particular embodiment of a systemthat identifies content of interest based on captioning data;

FIG. 2 is a diagram illustrating a particular embodiment of a searchresult page that displays one or more segments containing content ofinterest based on a search of captioning data;

FIG. 3 is a flowchart illustrating a particular embodiment of a methodof identifying content of interest; and

FIG. 4 is a block diagram of an illustrative embodiment of a generalcomputer system including components that are operable to identifycontent of interest based on captioning data.

DETAILED DESCRIPTION

Systems and methods of identifying videos containing content of interestare disclosed. The described techniques may enable a user to identifycontent of interest using captioning data (e.g., closed captioning data,open captioning data, and/or subtitle data) and contextual informationof video content, such as recordings stored at a digital video recorder(DVR) and video on demand (VOD). Segments of the video content thatcontain the content of interest may be presented to the user in an orderof relevance. Thus, the time needed for a user to identify a relevantvideo segment may be reduced as compared to the user manually searchingthrough the video content available to the user.

A captioning data analysis server may extract captioning data of videocontent that is available to users. The captioning data analysis servermay analyze the captioning data to identify particular captioning datathat matches one or more context sensitive terms associated with thevideo content. For example, the one or more context sensitive terms maybe received from one or more content providers. To illustrate, thecaptioning data analysis server may identify the context sensitive termsbased on electronic programming guide (EPG) data. The captioning dataanalysis server may identify one or more segments of the video contentthat are associated with particular captioning data.

The captioning data analysis server may use a set of factors todetermine contextual information of the one or more segments. The set offactors may include a presentation format of particular video content,particular phrases in the captioning data, qualifying words in proximity(e.g., within 5 words) to a word in the captioning data that matches thecontext sensitive term, disqualifying words in proximity to the word inthe captioning data that matches the context sensitive term, elapsedtime of the particular video content, a segment viewing frequency, wordsassociated with the context sensitive term, or a combination thereof.

Based on the contextual information, the captioning data analysis servermay determine whether the one or more segments of the video contentcontain content of interest. The captioning data analysis server maystore (e.g., in a database) a corresponding searchable tag associatedwith each segment that is determined to contain the content of interest.The captioning data analysis server may also assign a correspondingrelevancy score to each searchable tag based on the context information.

A search server may receive a search request from a user device tolocate particular segment(s) of video content that match one or morekeywords or phrases in the search request. The search server may accessthe database to search the searchable tags. The search server maygenerate a search result list including searchable tags that match theone or more keywords or phrases. In a particular embodiment, the searchresult list includes a single search tag that has the highest relevancyscore. In another particular embodiment, searchable tags are displayedin the search result list in order of relevancy score. For example, thecontent search server may list a first searchable tag with the highestrelevancy score (among searchable tags that match the one or morekeywords or phrases) as a first search result in the search result list.The content search server may list a second searchable tag with thesecond highest relevancy score as a second search result that followsthe first search result.

When a selection of a particular searchable tag in the search resultlist is received, an advertisement may be displayed prior to displayinga particular segment corresponding to the particular searchable tag.During the display of the advertisement, trick play functionality (e.g.,fast forwarding, pausing, rewinding, skip forwarding, skip backwarding,or a combination thereof) may be disabled. The trick play functionalitymay be enabled after the advertisement finishes playing.

In a particular embodiment, a method includes identifying, at acomputing device, multiple segments of video content based on a contextsensitive term. Each segment of the multiple segments is associated withcaptioning data of the video content. The method also includesdetermining, at the computing device, contextual information of a firstsegment of the multiple segments based on a set of factors. The methodfurther includes comparing first contextual information to particularcontextual information that corresponds to content of interest. Themethod further includes, in response to a determination that the firstcontextual information matches the particular contextual information,storing a first searchable tag associated with the first segment in adatabase.

In a particular embodiment, an apparatus includes a processor and amemory coupled to the processor. The memory includes instructions that,when executed by the processor, cause the processor to performoperations that include identifying, at a computing device, multiplesegments of video content based on a context sensitive term. Eachsegment of the multiple segments is associated with captioning data ofthe video content. The operations also include determining, at thecomputing device, contextual information of a first segment of themultiple segments based on a set of factors. The operations furtherinclude comparing the first contextual information to particularcontextual information that corresponds to content of interest. Theoperations further include, in response to a determination that thefirst contextual information matches the particular contextualinformation, storing a first searchable tag associated with the firstsegment.

In a particular embodiment, a computer readable storage device storesinstructions that, when executed by a processor, cause the processor toperform operations that include identifying, at a computing device,multiple segments of video content based on a context sensitive term.Each segment of the multiple segments is associated with captioning dataof the video content. The operations also include determining, at thecomputing device, contextual information of a first segment of themultiple segments based on a set of factors. The operations furtherinclude comparing the first contextual information to particularcontextual information that corresponds to content of interest. Theoperations further include, in response to a determination that thefirst contextual information matches the particular contextualinformation, storing a first searchable tag associated with the firstsegment.

Thus, systems and methods described herein may enable a user to identifysegments, presented in an order of relevance, that contain content ofinterest to the user based on captioning data. For example, a system maydetermine that contextual information of content of interest to a usercorresponds to musical performances of a particular singer based on userinput and other information. When the user searches for video content ofthe particular singer, the system may identify and present to the usersegments of video content that correspond to musical performances of theparticular singer. Thus, the time needed for the user to identify arelevant video segment may be reduced as compared to the user manuallysearching through the video content available to the user.

Referring to FIG. 1, a particular embodiment of a system 100 that isoperable to identify content of interest based on captioning data isillustrated. The system 100 includes a captioning data analysis server102, a searchable tag database 104, a search analysis server 106, and auser device 108. The user device 108 may include a tablet computer, asmartphone, a laptop computer, a personal digital assistant (PDA)device, or other computing device. In a particular embodiment, thecaptioning data analysis server 102 and the search analysis server 106are incorporated as a single entity (e.g., a single server).

During operation, the captioning data analysis server 102 may receivevideo content 114 from a plurality of sources, such as a remote storageserver 112 of a first video content provider, a remote storage server ofa second video content provider (not shown), etc. The captioning dataanalysis server 102 may receive the video content 114 via a network 126(e.g., an Internet protocol television network, a wireless network, awired network, etc.). The captioning data analysis server 102 mayextract captioning data of the video content 114. For example, when thecaptioning data is hardcoded into the video content 114, the captioningdata analysis server 102 may extract the captioning data via an opticalcharacter recognition process. As another example, the captioning datamay be transmitted to the captioning data analysis server 102 as a datastream or packets that are separate from the video content 114. Thecaptioning data may include closed captioning data, open captioningdata, and/or subtitle data.

The captioning data analysis server 102 may analyze the captioning datato generate searchable tags 116 to be stored in the searchable tagdatabase 104. For example, the captioning data analysis server 102 mayidentify particular captioning data that matches one or more contextsensitive terms associated with the video content 114. The captioningdata analysis server 102 may generate the one or more context sensitiveterms based on electronic programming guide (EPG) data 124 that isreceived from a content provider. In addition or alternatively, thecaptioning data analysis server 102 may receive the one or more contextsensitive terms (e.g., context sensitive terms 126) from one or morecontent providers. In a particular embodiment, the captioning dataanalysis server 102 accesses different sources that contain videocontent available to the user to generate the one or more contextsensitive terms. For example, the captioning data analysis server 102may access a first video stored in a digital video recorder (DVR) 110 togenerate the one or more context sensitive terms. The captioning dataanalysis server 102 may also access a second video stored in a remotestorage server 112 to generate the one or more context sensitive terms.In a particular embodiment, the captioning data analysis server 102concurrently accesses the DVR 110 and the remote storage server 112. Theone or more context sensitive terms may correspond to information thatis of interest to a user. For example, one or more context sensitiveterms may include a name of a person, a name of a musical album, a nameof a sports team, a brand name, a product name, etc.

The captioning data analysis server 102 may generate searchable tagsthat are specific to the video content 114 based on metadata (e.g.,dates, types of video content, etc.) of the video content 114. Forexample, when the captioning data analysis server 102 identifies thevideo content 114 as news broadcasting using the metadata, thecaptioning data analysis server 102 may apply a particular taggingscheme (e.g., a news tagging scheme) to generate searchable tagsassociated with the video content 114. The captioning data analysisserver 102 may use certain types of phrases, indicators, etc. that arespecific to news broadcasting to generate searchable tags associatedwith news broadcasting (and the news tagging scheme). The captioningdata analysis server 102 may also label searchable tags based on types(e.g., news broadcast, sporting broadcast, talk shows, etc.) of videocontent, so that when a user searches for content of a certain type(e.g., news broadcast, sporting broadcast, talk shows, etc.), the searchanalysis server 106 may identify video segments corresponding tosearchable tags that match the type of content.

To generate searchable tags, the captioning data analysis server 102 mayidentify segments of the video content 114 that contain the particularcaptioning data corresponding to the content of interest based on theone or more context sensitive terms. For example, a first contextsensitive term identified based on EPG data of a talk show may be“singer X” and three segments of the video content 114, where the videocontent 114 corresponds to a late night talk show, may containcaptioning data that matches the first context sensitive term “singerX”. Captioning data of a first segment of the three segments may be“tonight, our guests include singer X, singer Y, and singer Z.”Captioning data of a second segment of the three segments may be “singerX will be performing her latest hit song later on in the show.”Captioning data of a third segment of the three segments may be “ladiesand gentlemen, singer X is going to perform her latest hit song now.”The captioning data analysis server 102 may determine contextualinformation related to each of the three segments to identify segmentsthat include content of interest. For example, the captioning dataanalysis server 102 may generate the contextual information as textstrings. The context sensitive terms may vary based on types (e.g., newsbroadcast, sporting broadcast, talk shows, etc.) of video content. Forexample, video content associated with a talk show may have differentcontext sensitive terms than video content associated with a sportingevent. Context sensitive terms for a talk show may be names of guests(e.g., “singer X”), album names, etc. Context sensitive terms for asporting event may be “touchdown,” “slam dunk,” etc.

To determine whether a segment includes content of interest, thecaptioning data analysis server 102 may compare contextual informationof the segment to particular contextual information of a particularsegment having the content of interest. For example, the particularcontextual information may correspond to a performance of singer X. Whenthe captioning data analysis server 102 determines that the contextualinformation of the segment corresponds to merely a mention of singer Xduring a conversation, the data analysis server 102 may determine thatthe contextual information does not match the particular contextualinformation. Thus, the data analysis server 102 may determine that thesegment does not contain content of interest. However, when thecaptioning data analysis server 102 determines that the contextualinformation of the segment matches the particular contextual informationof the particular segment (e.g., the contextual information of thesegment corresponds to a performance of singer X), the data analysisserver 102 may determine that the segment contains content of interest.

As used herein, contextual information of a segment “matches” theparticular contextual information when a text string corresponding tothe contextual information is determined to match a text stringcorresponding to the particular contextual information using languageanalysis (e.g., fuzzy logic). For example, a text string correspondingto contextual information of content of interest may be “performance bysinger X” and “singer X” may be a search keyword. The data analysisserver 102 may identify four segments (e.g., a first segment, a secondsegment, a third segment, and a fourth segment) that contain captioningdata matching the search keyword “singer X”. A first text stringcorresponding to contextual information of the first segment may be“performance by singer X.” A second text string corresponding tocontextual information of the second segment may be “singer Xperforming.” A third text string corresponding to contextual informationof the third segment may be “singer X singing.” A fourth text stringcorresponding to contextual information of the fourth segment may be“singer X interview.”

The contextual information of the first segment matches the contextualinformation of content of interest because the first text string matchesthe text string of the contextual information of content interestverbatim. The contextual information of the second segment matches thecontextual information of content interest because the languageanalysis/fuzzy logic may determine that “performing” is equivalent to“performance”. In a particular embodiment, equivalence may be determinedusing a lookup table stored at the data analysis server 102. Thecontextual information of the third segment matches the contextualinformation of content of interest because the language analysis/fuzzylogic may determine that “singing” is a synonym of “performance.” In aparticular embodiment, synonyms may be determined using the lookuptable. The contextual information of the fourth segment does not matchthe contextual information of content of interest because the languageanalysis/fuzzy logic may determine that “interview” is not equivalent to“performance” nor is a synonym of “performance.” Determination ofcontextual information of a segment is described in further detailbelow.

In an embodiment, the captioning data analysis server 102 determinescontextual information corresponding to content of interest based onfeedback from one or more users of video content during a trainingsequence. As an example, during the training sequence, the captioningdata analysis server 102 may provide to one or more users a plurality ofsegments from the video content 114 or from other video content (e.g., aplurality of episodes of the same talk show) and may request the usersto indicate which segments include certain content of interest. Thecaptioning data analysis server 102 may receive selections of firstparticular segments that the one or more users indicate contain thecontent of interest. The captioning data analysis server 102 may alsoreceive selections of second particular segments that the one or moreusers indicate as not containing the content of interest.

During the training sequence, the captioning data analysis server 102may determine individualized contextual information that corresponds tothe content of interest for each type of the video content 114. As anexample, the captioning data analysis server 102 may provide a pluralityof segments from a first video (e.g., a talk show) to the one or moreusers to determine first contextual information of the first video thatcorresponds to the content of interest. The first contextual informationmay be specific to the first video. The captioning data analysis server102 may also provide a plurality of segments from a second video (e.g.,a movie) to the one or more users to determine second contextualinformation of the second video that corresponds to the content ofinterest. The second contextual information may be specific to thesecond video. The first contextual information may be different than thesecond contextual information.

The captioning data analysis server 102 may analyze selected segments(e.g., segments that correspond to the content of interest and segmentsthat do not correspond to the content of interest) to generate a set offactors that are used to determine contextual information of segments ofthe video content 114. The set of factors may be descriptive of a userpreference for content of interest. For example, the captioning dataanalysis server. 102 may use recurring common words or phrases inproximity to context sensitive terms in the selected segments togenerate the set of factors. As an example, the captioning data analysisserver 102 may analyze captioning data of multiple segments that containa particular context sensitive term to identify words or phrases, inproximity (e.g., within 5 words) to the particular context sensitiveterm, that appear whenever the context sensitive term appears. Forexample, in the case of musical performances, such phrases may include“performing now” or “please welcome.” The captioning data analysisserver 102 may also use elapsed time of the particular segments (e.g.,twenty minutes into a show) to generate the set of factors. For example,musical performances may be determined to occur at least 20 minutes intoa talk show. The captioning data analysis server 102 may use otherinformation of the particular segments to generate the set of factors.Additional factors to determine content of interest are described infurther detail herein.

When the captioning data analysis server 102 has identified segments ofthe video content 114 corresponding to content of interest based on theset of factors, the captioning data analysis server 102 may generate asearchable tag corresponding to each of the identified segments andstore the searchable tags in the searchable tag database 104. Forexample, the captioning data analysis server 102 may determine that afirst segment and a second segment of video content correspond to thecontent of interest and that a third segment does not contain thecontent of interest (e.g., the third segment does not satisfy any of theset of factors). The captioning data analysis server 102 may store afirst searchable tag associated with the first segment and a secondsearchable tag associated with the second segment in the searchable tagdatabase 104.

Since the third segment does not correspond to the content of interest,the third segment may not be associated with a searchable tag. Instead,the captioning data analysis server 102 may store an identification ofthe third segment to a second database for subsequent use. Thecaptioning data analysis server 102 may also store identifications ofthe first segment and the second segment to the second database. Forexample, during a subsequent training sequence, the captioning dataanalysis server 102 may determine that the first segment and the thirdsegment correspond to the content of interest based on user feedback andthat the second segment does not correspond to the content of interestbecause the user's preference for content of interest has changed. Inthis case, the captioning data analysis server 102 may remove the secondsearchable tag from the searchable tag database 104 and store a thirdsearchable tag associated with the third segment in the searchable tagdatabase 104.

The captioning data analysis server 102 may compute and assign acorresponding relevancy score to each searchable tag based on the set offactors. The captioning data analysis server 102 may compute thecorresponding relevancy score by summing a corresponding weight of eachof the set of factors that apply to the segment. For example, a segmentviewing frequency factor may have a weight of 0.5 and a qualifying wordsfactor may have a weight of 0.2. When a segment has the highest segmentviewing frequency and captioning data of the segment includes qualifyingwords, a relevancy score of a searchable tag associated with the segmentmay be 0.7. When another segment has captioning data that includesqualifying words but does not have the highest segment viewingfrequency, a relevancy score of a searchable tag associated with theother segment may be 0.2. The captioning data analysis server 102 mayadjust weights during subsequent training sequences (e.g., based on userfeedback or based on an algorithm update).

The searchable tag database 104 may store searchable tags associatedwith video content available to a user (e.g., the video content 114,recorded video content stored locally in the DVR 110, etc.). Thesearchable tag database 104 may store particular searchable tags thatare associated with a single video. Each searchable tag may include acorresponding context sensitive term (e.g., “singer X”), a time indexassociated with a corresponding segment (e.g., an offset time thatindicates the beginning of the corresponding segment in a video), asegment identification, one or more associations, a correspondingrelevancy score, a classification label, aging factor (e.g., half life),or a combination thereof. For example, a first searchable tag mayinclude the context sensitive term “singer X,” a time index of00:17-00:25, a segment identification of “talk show A,” “music” and“female singer” as associations, a relevancy score of 0.9, and/or aclassification label “entertainment”. A second searchable tag mayinclude the context sensitive term “president Y,” a time index of00:01-00:04, a segment identification of “news show B,” a relevancyscore of 0.3, and/or a classification label “news.” A relevancy score ofa searchable tag may be used by the search analysis server 106 togenerate relevant search results. The relevancy score may be adjustedbased on an associated aging factor. An aging factor may vary based on aclassification label of a searchable tag. For example, a searchable tagassociated with news content may lose relevancy much more quickly than asearchable tag associated with entertainment content. As an example, afirst particular searchable tag associated with news content has arelevancy score of 0.9 when the first particular searchable tag isinitially generated, the relevancy score of the first particularsearchable tag may be 0.1 after 7 days due to the aging factor. Asanother example, a second particular searchable tag associated withentertainment content may have a relevancy score of 0.9 when the secondparticular searchable tag is generated initially, the relevancy score ofthe second particular searchable tag may be 0.8 after 7 days due to theaging factor.

In a particular embodiment, the searchable tag database 104 is updatedperiodically (e.g., every day, every 30 minutes, etc.). Alternatively,the searchable tag database 104 may be updated whenever the captioningdata analysis server 102 receives (or detects) new video content. In aparticular embodiment, the searchable tag database 104 is generated assoon as the data analysis server 102 receives the video content 114. Inanother particular embodiment, the searchable tag database 104 isgenerated upon receipt of a search request from the user device 108.

When a user searches for particular content of interest, the user device108 may present two search options to the user. A first search optionmay be an assisted search and a second search option may be a manualsearch. When the user selects the assisted search option, the user maysearch for the particular content of interest by communicating with thesearch analysis server 106 via the user device 108. For example, theuser may enter information associated with particular content ofinterest into a content search application running on the user device108. The user device 108 may transmit a search request 118, generated bythe content search application, to the search analysis server 106. Thesearch request 118 may include one or more search keywords that the userentered or selected to indicate the particular content of interest.Based on the search request 118, the search analysis server 106 maysearch a subset of searchable tags in the searchable tag database 104 toidentify one or more particular searchable tags associated with segmentsof the video content that correspond to the particular content ofinterest.

The subset of searchable tags may be associated with a subset of thevideo content 114 that is accessible to the user. The search analysisserver 106 may identify the subset of video content accessible to theuser based on user account information associated with the user device108 or the user. For example, the subset of video content may includerecordings of video content stored locally in the DVR 110, recordings ofvideo content stored remotely (e.g., networked DVR recordings), video ondemand (e.g., stored in the remote storage server 112), other useraccessible video content, or any combination thereof. The DVR 110 may bepart of a set-top box device that incorporates DVR functionality.

The search analysis server 106 may compare the one or more searchkeywords to corresponding context sensitive terms and correspondingassociations (e.g., a first association generated based on the contextsensitive term of the first searchable tag 120 and a second associationgenerated based on the context sensitive term of the second searchabletag 122) to identify the one or more particular searchable tags. Forexample, an association of the context sensitive term “singer X” may be“jazz musician”. As another example, an association of a contextsensitive term “basketball” may be “sport.” When the one or more searchkeywords match a corresponding context sensitive term and/or acorresponding association in a searchable tag (e.g., the firstsearchable tag 120 and/or the second searchable tag 122), the searchanalysis server 106 may determine that the searchable tag corresponds tothe particular content of interest. For example, the search analysisserver 106 may determine that a first searchable tag 120 and a secondsearchable tag 122 correspond to the particular content of interest. Thesearch analysis server 106 may generate a search result list 124 basedon the first searchable tag 120 and/or the second searchable tag 122 andtransmit the search result list 124 to the user device 108. The searchresult list 124 is further described with reference to FIG. 2.

When the user selects one of the searchable tags listed in the searchresult list 124 via an input device (e.g., a keyboard or a touch screen)of the user device 108, the search analysis server 106 may receive theselection from the user device 108 and direct an advertisement to bedisplayed at a display device (e.g., a television 126 or a display ofthe user device 108) prior to playback of a segment associated with theselected searchable tag. For example, the selected segment may be asegment of a recorded video stored in the DVR 110 connected to thetelevision 126. The search analysis server 106 may transmit theadvertisement to the DVR 110 and direct the DVR 110 to display theadvertisement via the television 126 prior to displaying the selectedsegment at the television 126. The search analysis server 106 mayreceive the advertisement from an advertisement server (not shown inFIG. 1).

During the display of the advertisement, trick play functionality (e.g.,fast forwarding, pausing, rewinding, skip forwarding, and/or skipbackwarding) may be disabled. For example, the search analysis server106 may transmit a first command to the DVR 110 to instruct the DVR 110to refrain from responding to commands from a remote control of the DVR110 regarding the trick play functionality during the display of theadvertisement. The trick play functionality may be enabled after theadvertisement is completed (e.g., the first command or a secondsubsequent command may instruct the DVR 110 to be responsive to commandsfrom the remote control regarding the trick play functionality after theadvertisement is completed). After the display of the advertisement, theselected segment may begin playback at the beginning of the selectedsegment. For example, the selected segment may start at the 03:00 markof a video. The selected segment may be displayed starting playback atthe 03:00 mark of the video instead of starting at 00:00 (i.e., thebeginning of the video).

When the user selects the manual search option, the user device 108 maypresent a list of video content that is available to the user and theuser may manually search through the list of video content (e.g., bywatching each of the video content items on the list) to identify theparticular content of interest. During the manual search, anadvertisement may not be displayed prior to playback of the videocontent and trick-play functionality may remain enabled.

As described in FIG. 1, the captioning data analysis server 102 maydetermine contextual information of segments of the video content 114using a set of factors. The set of factors may include a presentationformat, particular phrases in the captioning data, qualifying words inproximity to a word in the captioning data that matches the contextsensitive term, disqualifying words in proximity to the word in thecaptioning data that matches the context sensitive term, elapsed time ofsegments, segment viewing frequency, words associated with the contextsensitive term, or a combination thereof.

In an example where the set of factors includes a presentation format,the video content 114 may include a talk show that follows apresentation format having three portions: introduction segment, guestconversation segment, and guest performance segment. When captioningdata matching the context sensitive term “singer X” is identified to bein the introduction segment of the talk show, the captioning dataanalysis server 102 may determine that contextual information of theintroduction segment corresponds to mentioning of singer X, but that theintroduction segment may not contain content of interest to a user(e.g., a performance by the singer X). When a particular segment isidentified to be in the guest performance segment, the captioning dataanalysis server 102 may determine that the context of the particularsegment corresponds to a performance of singer X and is of interest tothe user.

In an example where the set of factors includes particular phrases inthe captioning data, a talk show host may also utter the same phrases ineach episode. The same phrases may be used to determine contextassociated with segments where the same phrases are found in thecaptioning data. As an example, the talk show host may say a particularphrase “now it is time for a guest performance” prior to performance ofmusical guests of the talk show. When captioning data of the particularsegment is subsequent to captioning data corresponding to the particularphrase, the captioning data analysis server 102 may determine that thecontext of the particular segment corresponds to performance of singer Xand that the particular segment may contain the content of interest.When the captioning data of the particular segment is prior to thecaptioning data corresponding to the particular phrase, the captioningdata analysis server 102 may determine that the context of theparticular segment does not correspond to performance of singer X.

As another example, the context sensitive term may be “touchdown.” Asports show host may say a particular phrase “let us review today'stouchdown highlights” in every episode prior to showing clips oftouchdowns in different football games. When the captioning data of theparticular segment is subsequent to captioning data corresponding to theparticular phrase, the captioning data analysis server 102 may determinethat the context of the particular segment corresponds to showing oftouchdown clips.

In an example where the set of factors includes qualifying words inproximity to a word in the captioning data that matches the contextsensitive term, the captioning data analysis server 102 may analyze tenwords that are prior to the context sensitive term “singer X” and tenwords that are subsequent to “singer X” in each segment to determine thecontextual information of the three segments. For example, theparticular context that corresponds to the content of interest may beperformance of singer X. When particular words that indicate that singerX is about to perform a song, the particular words may be qualifyingwords. Words such as “performing now” or “welcome . . . to perform . . .new song” in proximity of “singer X” may indicate that the performanceof singer X is about to begin. Thus, a segment corresponding tocaptioning data containing the qualifying words may indicate that thesegment may contain the content of interest. It should be understoodthat the particular proximity may correspond to any number of wordsprior to and/or subsequent to the context sensitive term.

In an example where the set of factors includes disqualifying words inproximity to the word in the captioning data that matches the contextsensitive term, the captioning data analysis server 102 may analyzetwenty words that are prior to the context sensitive term “singer X” andtwenty words that are subsequent to “singer X” in each segment todetermine the context of the three segments. For example, when theparticular words that indicate that singer X is not about to perform asong, the particular words may be disqualifying words. Words such as“perform . . . later on,” “perform . . . in our next segment,” or“perform . . . following singer A's performance” in proximity of “singerX” may indicate that the performance of singer X is not about to begin.Thus, a segment corresponding to captioning data containing thedisqualifying words may indicate that the segment may not contain thecontent of interest. It should be understood that the particularproximity may correspond to any number of words prior to and/orsubsequent to the context sensitive term.

In an example where the set of factors includes elapsed time ofsegments, when elapsed time of a particular segment (e.g., the firstsegment) matches a particular elapsed time that is generated usingfeedback from the one or more users, the captioning data analysis server102 may determine that the particular segment may contain the content ofinterest. For example, during the training sequence, a first segment ofa talk show that a user has indicated as containing content of interestmay have a time index of 05:00 (e.g., the segment begins 5 minutes afterthe beginning of the talk show). During a search for content ofinterest, a second segment containing captioning data that matches asearch keyword and that has a time index of 05:00 may be identified ascontaining content of interest based at least partially on the timeindex of the second segment matching the time index of the firstsegment.

In an example where the set of factors includes segment viewingfrequency, the captioning data analysis server 102 may compare thenumber of times that each of the three segments has been viewed. Thecaptioning data analysis server 102 may determine that the particularsegment with the highest number of views (as compared to the other twosegments) contains the content of interest. The captioning data analysisserver 102 may receive information on the segment viewing frequency froman external source (e.g., a server of a content provider).

In an example where the set of factors includes words associated withthe context sensitive term, the words of the context sensitive term“singer X” may include album names of singer X, song names of singer X,concert names of singer X, other work of singer X, etc. Based on the setof factors, the captioning data analysis server 102 may determinecorresponding contextual information of the three segments. For example,based on the set of factors, the captioning data analysis server 102 maydetermine that contextual information of the first segment and thesecond segment corresponds to mentioning of singer X, and thatcontextual information of the third segment corresponds to a performanceby singer X.

Thus, the system 100 may enable a user to identify content of interestusing captioning data and contextual information of video content thatis available to the user. Thus, time needed for a user to identifyrelevant segments may be reduced as compared to the user identifying therelevant segments manually.

FIG. 2 illustrates a particular embodiment of a search result page 200that displays one or more segments containing the content of interestbased on a search of the captioning data. The search result page 200displays one or more search results that match the content of interest.For example, the search result page 200 includes a first search result202 and a second search result 204. The first search result 202 may begenerated based on the first searchable tag 120 of FIG. 1. The secondsearch result 204 may be generated based on the second searchable tag122 of FIG. 1.

The search results 202 and 204 may be ranked based on a relevancy scoreof a corresponding searchable tag. For example, when the first relevancyscore of the first searchable tag 120 is greater than the secondrelevancy score of the second searchable tag 122, the first searchresult 202 may be listed as a first (e.g., top) item in the searchresult page 200 and the second search result 204 may be listed as asecond (e.g., lower) item in the search result page 200 that follows thefirst item. In a particular embodiment, the search result page 200 onlyincludes one search result (e.g., only the first search result 202 orthe second search result 204) that corresponds to a searchable taghaving the highest relevancy score among all the searchable tags thatcorrespond to the content of interest. Each search result may include aplurality of portions. For example, the first search result 202 mayinclude a first segment portion 206 and a first time index portion 208.The first segment portion 206 may display captioning data associatedwith the first searchable tag 120. The first time index portion 208 maydisplay a time index associated with the first searchable tag 120 (e.g.,a time at which a segment associated with the first searchable tagbegins in a video). The second search result 204 may include a secondsegment portion 210 and a second time index portion 212. The secondsegment portion 210 may display captioning data associated with thesecond searchable tag 122. The second time index portion 212 may displaya time index associated with the second searchable tag 122. The searchresult page 200 may also include an advertisement portion 214. Theadvertisement portion 214 may display an advertisement that is generatedbased on at least one of the search results 202 and 204. Theadvertisement may also be generated based on one or more search keywordsin the search request.

FIG. 3 illustrates a particular embodiment of a method 300 ofidentifying content of interest. The method 300 includes identifying, ata computing device, multiple segments of video content based on acontext sensitive term, at 302. Each segment of the multiple segments isassociated with captioning data of the video content. For example,referring to FIG. 1, the captioning data analysis server 102 mayidentify segments of the video content 114 containing captioning datathat matches the first context sensitive term “singer X.”

The method 300 also includes determining, at the computing device,contextual information of a first segment of the multiple segments basedon a set of factors, at 304. Referring to FIG. 1, in an example wherethe set of factors include a presentation format, when a particularsegment is identified to be in the guest performance segment, thecaptioning data analysis server 102 may determine that the context ofthe particular segment corresponds to a performance of singer X, and isthus of interest to the user (i.e., the particular segment is orincludes content of interest).

The method 300 further includes comparing the first contextualinformation to particular contextual information that corresponds tocontent of interest, at 306. For example, referring to FIG. 1, theparticular contextual information of content of interest may correspondto a performance of singer X. When the captioning data analysis server102 determines that the contextual information of the segmentcorresponds to merely a mention of singer X during a conversation, thedata analysis server 102 may determine that the segment does not containcontent of interest. However, when the captioning data analysis server102 determines that the contextual information of the segmentcorresponds to a performance of singer X, the data analysis server 102may determine that the segment contains content of interest.

The method 300 further includes, in response to a determination that thefirst contextual information matches the particular contextualinformation, storing a first searchable tag associated with the firstsegment, at 308. For example, referring to FIG. 1, when the captioningdata analysis server 102 has identified segments of the video content114 corresponding to the content of interest based on the set offactors, the captioning data analysis server 102 may generate asearchable tag (e.g., the searchable tag 116) corresponding to each ofthe identified segments and store the searchable tags in the searchabletag database 104. As an example, the first searchable tag may includethe context sensitive term “singer X,” a time index of 00:17-00:25, asegment identification of “talk show A,” “music” and “female singer” asassociations, and a relevancy score of 0.9.

In alternate embodiments, the method 300 may include more, fewer, ordifferent steps. For example, the method 300 may include storing asecond searchable tag associated with a second segment of the multiplesegments in the database. For example, referring to FIG. 1, thecaptioning data analysis server 102 may store the second searchable tagassociated with the second segment in the searchable tag database 104.The method 300 further includes assigning a first relevancy score to thefirst segment based on a set of factors. For example, referring to FIG.1, the captioning data analysis server 102 may compute and assign acorresponding relevancy score to each searchable tag based on the set offactors.

The method 300 may include assigning a second relevancy score to thesecond segment based on the set of factors. For example, referring toFIG. 1, the captioning data analysis server 102 may compute and assign acorresponding relevancy score to each searchable tag based on the set offactors. The method 300 may further include storing a thirdidentification of a third segment of the multiple segments, where thethird identification is not associated with a searchable tag based on asecond determination that the third segment does not include the contentof interest. For example, referring to FIG. 1, since the third segmentdoes not correspond to the content of interest, the third segment maynot be associated with a searchable tag. Instead, the captioning dataanalysis server 102 may store an identification of the third segment toa second database for subsequent use.

The method 300 may include receiving a search request from a userdevice. For example, referring to FIG. 1, the user device 108 maytransmit the search request 118, generated by the content searchapplication, to the search analysis server 106. The method 300 furtherincludes comparing a search keyword of the search request to the firstsearchable tag and the second searchable tag. For example, referring toFIG. 1, when the one or more search keywords matches a correspondingcontext sensitive term and/or a corresponding association in asearchable tag (e.g., the first searchable tag 120 and/or the secondsearchable tag 122), the search analysis server 106 may determine thatthe searchable tag corresponds to the particular content of interest.

When the first relevancy score is greater than the second relevancyscore, the method 300 may include providing a first indication of thefirst segment to the user device as a first item in a search result listand not providing a second indication of the second segment as a seconditem in the search result list that follows the first item. When thesecond relevancy score is greater than the first relevancy score, themethod 300 may include providing the second indication to the userdevice as the first item in the search result list, where the firstindication is provided as the second item in the search result list. Forexample, referring to FIG. 2, when the first relevancy score of thefirst searchable tag 120 is greater than the second relevancy score ofthe second searchable tag 122, the first search result 202 may be listedas a first item in the search result page 200 and the second searchresult 204 may be listed as a second item in the search result page 200that follows the first item. As another example, when the secondrelevancy score is greater than the first relevancy score, the secondsearch result 204 may be listed as the first item and the first searchresult 202 may be listed as the second item.

When the first relevancy score is greater than the second relevancyscore, the method 300 may include providing a first indication of thefirst segment to the user device, where a second indication of thesecond segment is not provided to the user device. When the secondrelevancy score is greater than the first relevancy score, the method300 may include providing the second indication to the user device, andnot providing the first indication to the user device. For example,referring to FIG. 2, the search result page 200 may be shown on adisplay of the user device 108 and may only include a single searchresult (e.g., only the first search result 202 or the second searchresult 204) that corresponds to a searchable tag having the highestrelevancy score among all the searchable tags that correspond to thecontent of interest.

The method 300 may include generating a first association based on thefirst searchable tag and generating a second association based on thesecond searchable tag, where comparing the search keyword to the firstsearchable tag and the second searchable tag includes comparing thesearch keyword to a first context sensitive term of the first searchabletag, to a second context sensitive term of the second searchable tag, tothe first association, and to the second association. For example,referring to FIG. 1, the search analysis server 106 may compare the oneor more search keywords to corresponding context sensitive terms andcorresponding associations (e.g., the first association generated basedon the context sensitive term of the first searchable tag 120 and thesecond association generated based on the context sensitive term of thesecond searchable tag 122) to identify the one or more particularsearchable tags.

When the first segment is accessed without receiving a search requestfrom a user device, the method 300 may include displaying the firstsegment without displaying an advertisement. For example, referring toFIG. 1, when the selected segment is accessed without performing asearch on the search analysis server 106 (e.g., no search request isreceived at the search analysis server 106), the advertisement may notbe displayed. When the search request containing a keyword that matchesa portion of the first searchable tag is received from the user device,the method 300 may include displaying the advertisement at a displaydevice prior to playback of the first segment at the display device,where trick play functionality of the advertisement is disabled, andwhere the trick play functionality of the first segment is enabled afterthe advertisement is displayed. For example, referring to FIG. 1, whenthe user selects one of the searchable tags listed in the search resultlist 124 via an input device of the user device 108, the search analysisserver 106 may receive the selection from the user device 108 and directan advertisement to be displayed at a display device (e.g., a television126 or a display of the user device 108) prior to playback of a segmentassociated with the selected searchable tag at the display device.During the display of the advertisement, trick play functionality (e.g.,fast forwarding, pausing, rewinding, skip forwarding, and/or skipbackwarding) may be disabled. The trick play functionality may beenabled after the display of the advertisement. Thus, the method 300 mayenable a user to identify the content of interest using captioning dataand contextual information of video content that is available to theuser. Thus, time needed for a user to identify relevant segments may bereduced as compared to the user identifying relevant segments manually.

FIG. 4 illustrates a particular embodiment of a general computer system400 including components that are operable to identify content ofinterest based on captioning data. The general computer system 400 mayinclude a set of instructions that can be executed to cause the generalcomputer system 400 to perform any one or more of the methods orcomputer based functions disclosed herein. The general computer system400 may operate as a standalone device or may be connected, e.g., usinga network, to other computer systems or peripheral devices. For example,the general computer system 400 may include, be included within, orcorrespond to one or more of the components of the system 100, thecaptioning data analysis server 102 of FIG. 1, the search analysisserver 106, or a combination thereof described with reference to FIG. 1.

In a networked deployment, the general computer system 400 may operatein the capacity of a server or as a client user computer in aserver-client user network environment, or as a peer computer system ina peer-to-peer (or distributed) network environment. The generalcomputer system 400 may also be implemented as or incorporated intovarious devices, such as a personal computer (PC), a tablet PC, a STB, apersonal digital assistant (PDA), a customer premises equipment device,an endpoint device, a mobile device, a palmtop computer, a laptopcomputer, a desktop computer, a communications device, a wirelesstelephone, a web appliance, or any other machine capable of executing aset of instructions (sequential or otherwise) that specify actions to betaken by that machine. In a particular embodiment, the general computersystem 400 may be implemented using electronic devices that providevideo, audio, or data communication. Further, while one general computersystem 400 is illustrated, the term “system” shall also be taken toinclude any collection of systems or sub-systems that individually orjointly execute a set, or multiple sets, of instructions to perform oneor more computer functions.

As illustrated in FIG. 4, the general computer system 400 includes aprocessor 402, e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), or both. The processor 402 may be one or morecomponents (e.g., a processor) of the captioning data analysis server102, one or more components (e.g., a processor) of the search analysisserver 106, one or more components operable to identify content ofinterest using captioning data, or a combination thereof. Moreover, thegeneral computer system 400 may include a main memory 404 and a staticmemory 406, which can communicate with each other via a bus 408. Forexample, the main memory 404 may be one or more components (e.g., amemory) of the captioning data analysis server 102, one or morecomponents (e.g., a memory) of the search analysis server 106, one ormore components operable to store data, or a combination thereof. Asshown, the general computer system 400 may further include a videodisplay unit 410, such as a liquid crystal display (LCD), a flat paneldisplay, a solid state display, or a lamp assembly of a projectionsystem. Additionally, the general computer system 400 may include aninput device 412, such as a keyboard, and a cursor control device 414,such as a mouse. The general computer system 400 may also include adrive unit 416, a signal generation device 418, such as a speaker orremote control, and a network interface device 420. The general computersystem 400 may not include an input device (e.g., a server may notinclude an input device).

In a particular embodiment, as depicted in FIG. 4, the drive unit 416may include a computer-readable storage device 422 in which one or moresets of instructions 424, e.g. software, can be embedded. Thecomputer-readable storage device 422 may be random access memory (RAM),read-only memory (ROM), programmable read-only memory (PROM), erasablePROM (EPROM), electrically erasable PROM (EEPROM), register(s), harddisk, a removable disk, a compact disc read-only memory (CD-ROM), otheroptical disk storage, magnetic disk storage, magnetic storage devices,or any other storage device that can be used to store program code inthe form of instructions or data and that can be accessed by a computerand/or processor. The computer-readable storage device is not a signal.Further, the instructions 424 may embody one or more of the methods orlogic as described herein. The instructions 424 may be executable by theprocessor 402 to perform one or more functions or methods describedherein, such as the method described with reference to FIG. 3. In aparticular embodiment, the instructions 424 may reside completely, or atleast partially, within the main memory 404, the static memory 406,and/or within the processor 402 during execution by the general computersystem 400. The main memory 404 and the processor 402 also may include acomputer-readable storage device.

In an alternative embodiment, dedicated hardware implementations, suchas application specific integrated circuits, programmable logic arraysand other hardware devices, may be constructed to implement one or moreof the methods described herein. Various embodiments may broadly includea variety of electronic and computer systems. One or more embodimentsdescribed herein may implement functions using two or more specificinterconnected hardware modules or devices with related control and datasignals that can be communicated between and through the modules, or asportions of an application-specific integrated circuit. Accordingly, thepresent system encompasses software, firmware, and hardwareimplementations.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitingembodiment, implementations may include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing may be constructed toimplement one or more of the methods or functionality as describedherein.

The present disclosure contemplates a tangible computer-readable storagedevice 422 that stores the instructions 424 or receives, stores, andexecutes the instructions 424, so that a device connected to a network426 may communicate voice, video or data over the network 426. Forexample, the tangible computer-readable storage device 422 device mayinclude or be included within one or more of the components of thecaptioning data analysis server 102, one or more components of thesearch analysis server 106, one or more devices operable to identifycontent of interest using captioning data, or a combination thereofdescribed with reference to FIG. 1. While the tangible computer-readablestorage device 422 is shown to be a single device, the tangiblecomputer-readable storage device 422 may include a single medium ormultiple media, such as a centralized or distributed database, and/orassociated caches and servers that store one or more sets ofinstructions. The tangible computer-readable storage device 422 iscapable of storing a set of instructions for execution by a processor tocause a computer system to perform any one or more of the methods oroperations disclosed herein.

In a particular non-limiting, exemplary embodiment, the tangiblecomputer-readable storage device 422 may include a solid-state memorysuch as a memory card or other package that houses one or morenon-volatile read-only memories. Further, the tangible computer-readablestorage device 422 may be a random access memory or other volatilere-writable memory. Additionally, the tangible computer-readable storagedevice 422 may include a magneto-optical or optical medium, such as adisk or tapes or other storage device. Accordingly, the disclosure isconsidered to include any one or more of a tangible computer-readablestorage device and other equivalents and successor devices, in whichdata or instructions may be stored.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Accordingly, the disclosure and the figures are to be regarded asillustrative rather than restrictive.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar purpose may be substituted forthe specific embodiments shown. This disclosure is intended to cover anyand all subsequent adaptations or variations of various embodiments.

The Abstract of the Disclosure is provided with the understanding thatit will not be used to interpret or limit the scope or meaning of theclaims. In addition, in the foregoing Detailed Description, variousfeatures may be grouped together or described in a single embodiment forthe purpose of streamlining the disclosure. This disclosure is not to beinterpreted as reflecting an intention that the claimed embodimentsrequire more features than are expressly recited in each claim. Rather,as the following claims reflect, subject matter may be directed to lessthan all of the features of any of the disclosed embodiments. Thus, thefollowing claims are incorporated into the Detailed Description, witheach claim standing on its own as defining separately claimed subjectmatter.

The above-disclosed subject matter is to be considered illustrative, andnot restrictive, and the appended claims are intended to cover all suchmodifications, enhancements, and other embodiments, which fall withinthe scope of the present disclosure. Thus, to the maximum extent allowedby law, the scope of the present disclosure is to be determined by thebroadest permissible interpretation of the following claims and theirequivalents, and shall not be restricted or limited by the foregoingdetailed description.

What is claimed is:
 1. A method comprising: receiving, at a computingdevice, electronic program guide data, the electronic program guide datadescriptive of programs available from a content provider; determining,at the computing device and based on the electronic program guide data,a context sensitive term descriptive of video content of one of theprograms identified in the electronic program guide data; identifying,at the computing device, multiple segments of the video content thatcontain captioning data including the context sensitive term, andwherein the captioning data is distinct from the electronic programguide data; determining, at the computing device, first contextualinformation of a first segment of the multiple segments; in response todetermining the first contextual information, determining a first agingfactor, wherein the first aging factor is determined based on the firstcontextual information; in response to a determination that the firstcontextual information corresponds to content of interest, storing afirst searchable tag, the first searchable tag including the firstcontextual information and the first aging factor; responsive to a firstsearch at a first time, identifying a first relevancy score for thefirst searchable tag, the first relevancy score based on the firstcontextual information and the first aging factor; and responsive to asecond search at a second time, identifying a second relevancy score forthe first searchable tag, the second relevancy score based on the firstcontextual information and the first aging factor, wherein a differencein the first relevancy score and the second relevancy score is based onthe first aging factor and a period of time between the first time andthe second time.
 2. The method of claim 1, further comprising: storing asecond searchable tag and a second aging factor associated with a secondsegment of the multiple segments in the database; and responsive to asearch request: assigning a first relevancy score to the first segmentbased on a set of relevance factors and the first aging factor; andassigning a second relevancy score to the second segment based on theset of relevance factors and the first aging factor.
 3. The method ofclaim 1, further comprising: receiving a search request from a userdevice; determining a subset of searchable tags from among a pluralityof searchable tags, the subset of searchable tags determined based onuser account information associated with the user device; and comparinga search keyword of the search request to the subset of searchable tags.4. The method of claim 3, wherein the plurality of searchable tagsdescribes each tagged video segment in a database, and the subset ofsearchable tags describes a subset of the tagged video segment in thedatabase that are also stored on a personal video content recorderassociated with the user account information.
 5. The method of claim 3,wherein the plurality of searchable tags describes each tagged videosegment in a database, and the subset of searchable tags describes asubset of the tagged video segment in the database that are accessibleto the user device based on the user account information.
 6. The methodof claim 1, further comprising determining the first searchable tagbased on a tagging scheme that is selected based on a genre of the videocontent.
 7. The method of claim 3, wherein the user device includes asmartphone or a tablet computer.
 8. The method of claim 1, wherein thefirst contextual information of the first segment is determined based ona set of factors including: a presentation format of particular videocontent, elapsed time of the particular video content, a segment viewingfrequency, or a combination thereof.
 9. The method of claim 1, furthercomprising when a search request containing a keyword that matches aportion of the first searchable tag is received from a user device,displaying an advertisement at a display device prior to playback of thefirst segment at the display device, wherein trick play functionality ofthe advertisement is disabled, and wherein the trick play functionalityof the first segment is enabled after the advertisement is displayed.10. The method of claim 9, wherein the trick play functionality includesfast forwarding, pausing, rewinding, skip forwarding, skip backwarding,or a combination thereof.
 11. The method of claim 1, further comprising:storing the first contextual information at a server; and comparing thefirst contextual information stored at the server to second content ofinterest responsive to user input identifying the second content ofinterest.
 12. The method of claim 1, further comprising determining thefirst aging factor based on a genre of the video content.
 13. The methodof claim 12, wherein the first contextual information identifies a firstgenre associated with the first segment, wherein the first aging factoris a first value responsive to the first genre being news, wherein thefirst aging factor is a second value responsive to the first genre beingentertainment, and wherein the first value causes the first relevancescore to be adjusted at a faster pace than the second value.
 14. Anapparatus comprising: a processor; and a memory coupled to theprocessor, wherein the memory includes instructions that, when executedby the processor, cause the processor to perform operations, theoperations including: receiving electronic program guide data, theelectronic program guide data descriptive of programs available from acontent provider; determining, based on the electronic program guidedata, a context sensitive term descriptive of video content of one ofthe programs identified in the electronic program guide data;identifying multiple segments of the video content that containcaptioning data including the context sensitive term, and wherein thecaptioning data is distinct from the electronic program guide data;determining first contextual information of a first segment of themultiple segments; in response to determining the first contextualinformation, determining a first aging factor, wherein the first agingfactor is determined based on the first contextual information; inresponse to a determination that the first contextual informationcorresponds to content of interest, storing first searchable tag in adatabase, the first searchable tag including the first contextualinformation and the first aging factor; responsive to a first search ata first time, identifying a first relevancy score for the firstsearchable tag, the first relevancy score based on the first contextualinformation and the first aging factor; and responsive to a secondsearch at a second time, identifying a second relevancy score for thefirst searchable tag, the second relevancy score based on the firstcontextual information and the first aging factor, wherein a differencein the first relevancy score and the second relevancy score is based onthe first aging factor and a period of time between the first time andthe second time.
 15. The apparatus of claim 14, wherein the processor isa component of a smartphone or a tablet computer.
 16. The apparatus ofclaim 14, further comprising receiving user input identifying a searchtype from a plurality of search types including a manual search type andan assisted search type, wherein the determination that the firstcontextual information corresponds to the content of interest is maderesponsive to the user input identifying the assisted search type. 17.The apparatus of claim 14, wherein the operations further comprise whena search request containing a keyword that matches a portion of thefirst searchable tag is received from a user device, displaying anadvertisement at a display coupled to a set-top box device prior toplayback of the first segment at the set-top box device, wherein trickplay functionality of the advertisement is disabled, and wherein thetrick play functionality of the first segment is enabled after theadvertisement is displayed.
 18. The apparatus of claim 17, wherein thetrick play functionality includes fast forwarding, pausing, rewinding,skip forwarding, skip backwarding, or a combination thereof.
 19. Theapparatus of claim 14, wherein the video content includes a first videoand a second video, wherein the first video is stored at a digital videorecorder, and wherein the second video is stored at a remote storagedevice of a content provider.
 20. A computer-readable hardware devicestoring instructions that, when executed by a processor, cause theprocessor to perform operations comprising: receiving electronic programguide data, the electronic program guide data descriptive of programsavailable from a content provider; determining, based on the electronicprogram guide data, a context sensitive term descriptive of videocontent of one of the programs identified in the electronic programguide data; identifying multiple segments of the video content thatcontain captioning data including the context sensitive term, andwherein the captioning data is distinct from the electronic programguide data; determining first contextual information of a first segmentof the multiple segments; in response to determining the firstcontextual information, determining a first aging factor, wherein thefirst aging factor is determined based on the first contextualinformation; in response to a determination that the first contextualinformation corresponds to content of interest, storing a firstsearchable tag in a database, the first searchable tag including thefirst contextual information and the first aging factor; responsive to afirst search at a first time, identifying a first relevancy score forthe first searchable tag, the first relevancy score based on the firstcontextual information and the first aging factor; and responsive to asecond search at a second time, identifying a second relevancy score forthe first searchable tag, the second relevancy score based on the firstcontextual information and the first aging factor, wherein a differencein the first relevancy score and the second relevancy score is based onthe first aging factor and a period of time between the first time andthe second time.